Environmental stressors as a driver of the trait composition of benthic macroinvertebrate assemblages in polluted Iberian rivers
Bibliographic record
Abstract
We used the trait composition of macroinvertebrate communities to identify the effects of pesticides and multiple stressors associated with urban land use at different sites of four rivers in Spain. Several physical and chemical stressors (high metal pollution, nutrients, elevated temperature and flow alterations) affected the urban sites. The occurrence of multiple stressors influenced aquatic assemblages at 50% of the sites. We hypothesized that the trait composition of macroinvertebrate assemblages would reflect the strategies that the assemblages used to cope with the respective environmental stressors. We used RLQ and fourth corner analysis to address the relationship between stressors and the trait composition of benthic macroinvertebrates. We found a statistically significant relationship between the trait composition and the exposure of assemblages to environmental stressors. The first RLQ dimension, which explained most of the variability, clearly separated sites according to the stressors. Urban-related stressors selected taxa that were mainly plurivoltine and fed on deposits. In contrast, pesticide impacted sites selected taxa with high levels of egg protection (better egg survival), indicating a potentially higher risk for egg mortality. Moreover, the trait diversity of assemblages at urban sites was low compared to that observed in pesticide impacted sites, suggesting the homogenization of assemblages in urban areas.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".